The focus of this work is the Invariant Risk Minimization (IRM) framework, proposed in [1]}. The framework relates to domain generalization where access to the test distribution is not assumed. It is rooted in the theory of causality [2]} and links to invariance, which is useful for generalization [3]}, [4]}. In [5]}, the authors reformulate IRM via a game-theoretic approach, wherein the invariant representation corresponds to the Nash equilibrium of a game. While the IRM framework assumes only the invariance of the conditional expectation of the label given the representation, some follow-up works rely on stronger invariance assumptions [6]}, [7]}.
